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SWFL Fresh Producers

Public·5 SWFL Producers
Ezra Long
Ezra Long


Michael Simeone: Yeah exactly, it's June 26 today. Yesterday was was June 25. Helpful clarification. A consistent backdrop for the governor of our state during these briefings has been bar graphs. And so the idea that the governor of the state is going to be giving a briefing on a semi regular basis. And the backdrop is consistently bar graphs just kind of speaks to how important data and data visualization is. In our moment. I think it's important to address why data visualization can sometimes be a place where we stumble or become misinformed, because we're living right now, in a moment where these data visualizations, we're putting a lot of stock in them.

[S1E4] LA Times

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Michael Simeone: So what's one of the charts that we've seen tons and tons of times, especially for case rate, we see two different kinds. One is the line graph, which is just going to show cases over time. And that's where I think a lot of times people are talking about the curve. But the other one we see is actually just accounts of cases over time as skinny little bars that are all racked up from March all the way to the present moment. And that looks a lot like I don't know, Fitbit, telling you about how many steps you took in a day or how many hours of sleep that you got. I think we run into a lot of trouble when some of these tools look very similar to some of the apps that we use every day. It looks like a dashboard that gives us instant up to date information. It's not unreasonable to expect that they look similar. How come This Coronavirus information isn't instant and up to date? It's not like everybody understands how long Coronavirus testing takes, we wouldn't assume that automatically. To me this is confusing because our expectation is that it's instantaneous. Most the time when we interact with things that look like this, the latency period between event and reporting is a lot faster.

Michael Simeone: So a lot of times, we'll put the data into buckets, and then scale the color according to where the data is. And what all that means is, we might have one to 10, 10 to 20, 20 to 30, 30 to 40, right, if our maximum value is 40, and then we'll just shade in the map according to where the kind of counts fall according to certain locations, right. So if we can even call up the Arizona Department of Health Services map right now, by zip code map, where we can see what the case rates are by zip code or case totals, I'm sorry, per zip code, you know, some of them are shaded in darker because there's more cases and some of them are shaded and lighter because of other cases.

Michael Simeone: Yeah, and I keep coming back to this that, what decision are we supporting? By creating a dashboard like this? A lot of times dashboards are instruments that are designed for very specific purposes, the idea of a generally informative dashboard is a thing. But generally we want to think about at least some of the possible decisions that someone might want to make. So if we're talking about a COVID-19 dashboard, there's a small number of decisions that a lot of people are weighing right now. How much should I go out? You know, should I go to X number of businesses or what kinds of businesses? Should I wear a mask? All of these decisions are relatively straightforward. If we're all on the same page about the CDC right now, where these dashboards can become more useful is when states start to have very different kinds of situations. There are states where the curve is kind of declining right now. So maybe people are making slightly different decisions, but on plenty of these dashboards in states that have a lot of Coronavirus and states that don't have a lot of Coronavirus right now, relatively speaking, there's still not an explicit connection to behavior, which I find very unusual in looking At a dashboard and not having it linked to any kind of decision making, that somebody is going to make, right, we're observing the data, but that data is kind of in a vacuum. And so again, I just think it kind of sets it up there for ridicule or criticism, rather than helping connect it to some kind of evidence based practice.

Michael Simeone: Yeah, I mean, I think there should be some kind of subtext for charts at all times. And if not, there should be some kind of lengthy explanation about why we're all here, so to speak. So there should be a reason and a decision associated with this stuff. And you know, the other point you bring up is it's also interesting that not only are dashboards designed oftentimes to help support complex decisions with lots of interdependent factors, so that people who are making decisions can just, you know, more rapidly or in a more informed way make them they these are also right, we can't leave behind this idea about how visually ambiguous these things are with much more mundane things. If we didn't have Coronavirus labelled on any of these charts and graphs. It could just as easily look like sales figures from an online retail company. You know, we could just be looking at q2 sales in the state of Arizona by day, but it looks exactly the same as Coronavirus cases. That's not to say that every chart has to look exactly different right? Depending on what it's doing. That's That's impossible. But it does contribute to some of our expectations, right? It creates that vacuum so that without some kind of further explanation about why we're paying attention to this stuff, then we just default back to our normal expectations about looking at these things, which is Oh, just reporting it, and I want to know it, but we have to know what kind of decision we want to be making.

Shawn Walker: Yes, I've noticed that similar, sometimes the colors are different. And the design of the graphs, the way information is presented, how the dashboards load, it just kind of looks like they've all purchased the same product. And that's what they're using to display their data.

Michael Simeone: Right, So the more custom it is, the more it's going to cost you. Or we could use an off the shelf product and put it on Amazon Web Services. But if we do something that isn't bespoke, then we get all kinds of problems that we're talking about right now, which is the charts, raise a lot of questions. And then also occlude some of the data raising more questions. So we walk away with that vacuum you talked about, and that's an invitation to explain what we see, or to criticize what we see. And right now, the conspiracy theory thinking the misinformation that's kicking around, this is the stuff that's oftentimes flying in to serve as an explanation. For what's going on, or as really fuel for the criticism of what we see. I mean, we've talked about this about these dashboards. And I think there have been some, some kind of interesting points raised about what goes into making them and how reliable they are and what the consequences are for ambiguity, and occlusion of data. But how many people do you think actually use these dashboards? And take them seriously? 041b061a72


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